Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

The future of rare disease drug development: the rare disease cures accelerator data analytics platform (RDCA-DAP).

Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients. The future of rare disease drug development will hopefully rely on increased data sharing and collaboration among the entire rare disease ecosystem. One path to achieving this outcome has been the development of the rare disease cures accelerator, data analytics platform (RDCA-DAP) funded by the US FDA and operationalized by the Critical Path Institute. FDA intentions were clearly focused on improving the quality of rare disease regulatory applications by sponsors seeking to develop treatment options for various rare disease populations. As this initiative moves into its second year of operations it is envisioned that the increased connectivity to new and diverse data streams and tools will result in solutions that benefit the entire rare disease ecosystem and that the platform becomes a Collaboratory for engagement of this ecosystem that also includes patients and caregivers.

Role of Disease Progression Models in Drug Development.

The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided.

Risk assessment of therapeutic agents under consideration to treat COVID-19 in paediatric patients and pregnant women.

AIM: Repurposing strategies to address the COVID-19 pandemic have been accelerated. As both pregnant and paediatric patients are likely to be excluded from most planned investigations, the list of repurposed options and the available data on these drugs and vaccines provide a baseline risk assessment and identify gaps for targeted investigation. METHODS: Clinical trials have been searched and reviewed; 23 repurposed drugs and drug combinations and nine candidate vaccines have been assessed regarding the availability of relevant data in paediatrics and pregnant women and to evaluate expected or unanticipated risk. RESULTS: Thirteen of the repurposed drugs or drug combinations are indicated for use in paediatrics in some age category albeit for indications other than COVID-19; 10 of these are indicated for use in pregnant women. Even in cases where these drugs are indicated in the populations, source data from which safety and or dosing could be extrapolated for use in COVID-19 is sparse. Vaccine trials are ongoing and generally exclude pregnant women; only in a few instances have paediatric subgroups been planned for enrolment. Data from individual case studies and RWD may suggest that subpopulations of both paediatric patients and pregnant women may be more at risk, particularly those in an increased inflammatory state. CONCLUSION: In conjunction with more prospective collaboration, plans are evolving to ensure that we will be better prepared to address similar situations especially in paediatrics and pregnant women where experience is limited and actual practice relies heavily on leveraging data from other populations and indications.

Characterization of Intestinal and Hepatic CYP3A-Mediated Metabolism of Midazolam in Children Using a Physiological Population Pharmacokinetic Modelling Approach.

PURPOSE: Changes in drug absorption and first-pass metabolism have been reported throughout the pediatric age range. Our aim is to characterize both intestinal and hepatic CYP3A-mediated metabolism of midazolam in children in order to predict first-pass and systemic metabolism of CYP3A substrates. METHODS: Pharmacokinetic (PK) data of midazolam and 1-OH-midazolam from 264 post-operative children 1-18 years of age after oral administration were analyzed using a physiological population PK modelling approach. In the model, consisting of physiological compartments representing the gastro-intestinal tract and liver,intrinsic intestinal and hepatic clearances were estimated to derive values for bioavailability and plasma clearance. RESULTS: The whole-organ intrinsic clearance in the gut wall and liver were found to increase with body weight, with a 105 (95% confidence interval (CI): 5-405) times lower intrinsic gut wall clearance than the intrinsic hepatic clearance (i.e. 5.08 L/h (relative standard error (RSE) 10%) versus 527 L/h (RSE 7%) for a 16 kg individual, respectively). When expressed per gram of organ, intrinsic clearance increases with increasing body weight in the gut wall, but decreases in the liver, indicating that CYP3A-mediated intrinsic clearance and local bioavailability in the gut wall and liver do not change with age in parallel. The resulting total bioavailability was found to be age-independent with a median of 20.8% in children (95%CI: 3.8-50.0%). CONCLUSION: In conclusion, the intrinsic CYP3A-mediated gut wall clearance is substantially lower than the intrinsic hepatic CYP3A-mediated clearance in children from 1 to 18 years of age, and contributes less to the overall first-pass metabolism compared to adults.

Safety, dosing, and pharmaceutical quality for studies that evaluate medicinal products (including biological products) in neonates.

The study of medications among pediatric patients has increased worldwide since 1997 in response to new legislation and regulations, but these studies have not yet adequately addressed the therapeutic needs of neonates. Additionally, extant guidance developed by regulatory agencies worldwide does not fully address the specificities of neonatal drug development, especially among extremely premature newborns who currently survive. Consequently, an international consortium from Canada, Europe, Japan, and the United States was organized by the Critical Path Institute to address the content of guidance. This group included neonatologists, neonatal nurses, parents, regulators, ethicists, clinical pharmacologists, specialists in pharmacokinetics, specialists in clinical trials and pediatricians working in the pharmaceutical industry. This group has developed a comprehensive, referenced White Paper to guide neonatal clinical trials of medicines - particularly early phase studies. Key points include: the need to base product development on neonatal physiology and pharmacology while making the most of knowledge acquired in other settings; the central role of families in research; and the value of the whole neonatal team in the design, implementation and interpretation of studies. This White Paper should facilitate successful clinical trials of medicines in neonates by informing regulators, sponsors, and the neonatal community of existing good practice.

Pharmacologic rationale for the NK1R antagonist, aprepitant as adjunctive therapy in HIV.

BACKGROUND: Many HIV infected individuals with suppressed viral loads experience chronic immune activation frequently developing neurological impairment designated as HIV associated neurocognitive disorder (HAND). Adjunctive therapies may reduce HIV associated inflammation and therefore decrease the occurrence of HAND. METHODS: We have conducted in vitro, animal and clinical studies of the neurokinin 1 receptor (NK1R) antagonist aprepitant in HIV/SIV infection. RESULTS: Aprepitant inhibits HIV infection of human macrophages ex vivo with an ED50 ~ 5 µM. When administered at 125 mg once daily for 12 months to SIV-infected rhesus macaques, aprepitant reduced viral load by approximately tenfold and produced anti-anxiolytic effects. The anti-viral and anti-anxiolytic effects occur at approximately the third month of dosing; and the effects are sustained throughout the duration of drug administration. Protein binding experiments in culture media and animal and human plasma indicate that the free fraction of aprepitant is lower than previously reported supporting usage of higher doses in vivo. The analysis of blood samples from HIV positive individuals treated for 2 weeks with aprepitant at doses up to 375 mg demonstrated reduced levels of pro-inflammatory cytokines including G-CSF, IL-6, IL-8 and TNFα. Decreased pro-inflammatory cytokines may reduce HIV comorbidities associated with chronic inflammation. CONCLUSIONS: Our results provide evidence for a unique combination of antiretroviral, anti-inflammatory and behavioral modulation properties of aprepitant in vitro and in vivo. These results provide robust support for a clinical exposure target above that recommended for chemotherapy-induced nausea and vomiting. Doses up to 375 mg once daily in HIV-infected patients still elicit sub-therapeutic exposure of aprepitant though effective plasma concentrations can be achievable by proper dose modulation.

Potent obatoclax cytotoxicity and activation of triple death mode killing across infant acute lymphoblastic leukemia.

Survival in infants younger than 1 year who have acute lymphoblastic leukemia (ALL) is inferior whether MLL is rearranged (R) or germline (G). MLL translocations confer chemotherapy resistance, and infants experience excess complications. We characterized in vitro sensitivity to the pan-antiapoptotic BCL-2 family inhibitor obatoclax mesylate in diagnostic leukemia cells from 54 infants with ALL/bilineal acute leukemia because of the role of prosurvival BCL-2 proteins in resistance, their imbalanced expression in infant ALL, and evidence of obatoclax activity with a favorable toxicity profile in early adult leukemia trials. Overall, half maximal effective concentrations (EC50s) were lower than 176 nM (the maximal plasma concentration [Cmax] with recommended adult dose) in 76% of samples, whether in MLL-AF4, MLL-ENL, or other MLL-R or MLL-G subsets, and regardless of patients' poor prognostic features. However, MLL status and partner genes correlated with EC50. Combined approaches including flow cytometry, Western blot, obatoclax treatment with death pathway inhibition, microarray analyses, and/or electron microscopy indicated a unique killing mechanism involving apoptosis, necroptosis, and autophagy in MLL-AF4 ALL cell lines and primary MLL-R and MLL-G infant ALL cells. This in vitro obatoclax activity and its multiple killing mechanisms across molecular cytogenetic subsets provide a rationale to incorporate a similarly acting compound into combination strategies to combat infant ALL.

Paediatric models in motion: requirements for model-based decision support at the bedside.

Optimal paediatric pharmacotherapy is reliant on a detailed understanding of the individual patient including their developmental status and disease state as well as the pharmaceutical agents he/she is receiving for treatment or management of side effects. Our appreciation for size and maturation effects on the pharmacokinetic/pharmacodynamic (PK/PD) phenomenon has improved to the point that we can develop predictive models that permit us to individualize therapy, especially in the situation where we are monitoring drug effects or therapeutic concentrations. The growth of efforts to guide paediatric pharmacotherapy via model-based decision support necessitates a coordinated and systematic approach to ensuring reliable and robust output to caregivers that represents the current standard of care and adheres to governance imposed by the host institution or coalition responsible. Model-based systems which guide caregivers on dosing paediatric patients in a more comprehensive manner are in development at several institutions. Care must be taken that these systems provide robust guidance with the current best practice. These systems must evolve as new information becomes available and ultimately are best constructed from diverse data representing global input on demographics, ethnic / racial diversity, diet and other lifestyle factors. Multidisciplinary involvement at the project team level is key to the ultimate clinical valuation. Likewise, early engagement of clinical champions is also critical for the success of model-based tools. Adherence to regulatory requirements as well as best practices with respect to software development and testing are essential if these tools are to be used as part of the routine standard of care.

Population pharmacokinetics of midazolam and its metabolites in overweight and obese adolescents.

AIM: In view of the increasing prevalence of obesity in adolescents, the aim of this study was to determine the pharmacokinetics of the CYP3A substrate midazolam and its metabolites in overweight and obese adolescents. METHODS: Overweight (BMI for age ≥ 85(th) percentile) and obese (BMI for age ≥ 95(th) percentile) adolescents undergoing surgery received 2 or 3 mg intravenous midazolam as a sedative drug pre-operatively. Blood samples were collected until 6 or 8 h post-dose. Population pharmacokinetic modelling and systematic covariate analysis were performed using nonmem 7.2. RESULTS: Nineteen overweight and obese patients with a mean body weight of 102.7 kg (62-149.8 kg), a mean BMI of 36.1 kg m(-2) (24.8-55 kg m(-2)), and a mean age of 15.9 years (range 12.5-18.9 years) were included. In the model for midazolam and metabolites, total body weight was not of influence on clearance (0.66 l min(-1) (RSE 8.3%)), while peripheral volume of distribution of midazolam (154 l (11.2%)), increased substantially with total body weight (P < 0.001). The increase in peripheral volume could be explained by excess body weight (WTexcess ) instead of body weight related to growth (WTfor age and length ). CONCLUSIONS: The pharmacokinetics of midazolam and its metabolites in overweight and obese adolescents show a marked increase in peripheral volume of distribution and a lack of influence on clearance. The findings may imply a need for a higher initial infusion rate upon initiation of a continuous infusion in obese adolescents.

Predictors of arterial blood pressure control during deliberate hypotension with sodium nitroprusside in children.

BACKGROUND: Sodium nitroprusside (SNP) is used to decrease arterial blood pressure (BP) during certain surgical procedures. There are limited data regarding efficacy of BP control with SNP. There are no data on patient and clinician factors that affect BP control. We evaluated the dose-response relationship of SNP in infants and children undergoing major surgery and performed a quantitative assessment of BP control. METHODS: One hundred fifty-three subjects at 7 sites received a blinded infusion followed by open-label SNP during operative procedures requiring controlled hypotension. SNP was administered by continuous infusion and titrated to maintain BP control (mean arterial BP [MAP] within ±10% of clinician-defined target). BP was recorded using an arterial catheter. Statistical process control methodology was used to quantify BP control. A multivariable model assessed the effects of patient and procedural factors. RESULTS: BP was controlled an average 45.4% (SD 23.9%; 95% CI, 41.5%-49.18%) of the time. Larger changes in infusion rate were associated with worse BP control (7.99% less control for 1 µg·kg·min increase in average titration size, P = 0.0009). A larger difference between a patient's baseline and target MAP predicted worse BP control (0.93% worse control per 1-mm Hg increase in MAP difference, P = 0.0013). Both effects persisted in multivariable models. CONCLUSIONS: SNP was effective in reducing BP. However, BP was within the target range less than half of the time. No clinician or patient factors were predictive of BP control, although 2 inverse relationships were identified. These relationships require additional study and may be best coupled with exposure-response modeling to propose improved dosing strategies when using SNP for controlled hypotension in the pediatric population.

Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity.

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.

Advancing the utilization of real-world data and real-world evidence in clinical pharmacology and translational research-Proceedings from the ASCPT 2023 preconference workshop.

Real-world data (RWD) and real-world evidence (RWE) are now being routinely used in epidemiology, clinical practice, and post-approval regulatory decisions. Despite the increasing utility of the methodology and new regulatory guidelines in recent years, there remains a lack of awareness of how this approach can be applied in clinical pharmacology and translational research settings. Therefore, the American Society of Clinical Pharmacology & Therapeutics (ASCPT) held a workshop on March 21st, 2023 entitled "Advancing the Utilization of Real-World Data (RWD) and Real-World Evidence (RWE) in Clinical Pharmacology and Translational Research." The work described herein is a summary of the workshop proceedings.

Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis.

OBJECTIVE: Tacrolimus is used clinically for the long-term treatment of antirejection of transplanted organs in liver and kidney transplant recipients, although dose optimization is poorly managed. The aim of this study was to examine the association between tacrolimus pharmacokinetic variability and CYP3A4 and CYP3A5 genotypes by a population pharmacokinetic analysis based on routine drug monitoring data in adult renal transplant recipients. MATERIALS AND METHODS: Trough tacrolimus concentrations were obtained from 161 adult kidney transplant recipients after transplantation. The population pharmacokinetic analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 7.2. The CYP3A4*1G and CYP3A5*3 genetic polymorphisms from the patients studied were determined by direct sequencing using a validated automated genetic analyzer. RESULTS: A one-compartment model with first-order absorption and elimination adequately described the pharmacokinetics of tacrolimus. Covariates including CYP3A5*3 and CYP3A4*1G alleles and hematocrit were retained in the final model. The apparent clearance of tacrolimus was about two-fold higher in kidney transplant patients with higher enzymatic activity of CYP3A5*1 and CYP3A4*1G (with the CYP3A5*1/*1 or *1/*3 and CYP3A4*1/*1G or CYP3A4*1G/*1G) compared with those with lower enzymatic activity (CYP3A5*3/*3 and CYP3A4*1/*1). CONCLUSION: This is the first study to extensively determine the effect of CYP3A4*1G and CYP3A5*3 genetic polymorphisms and hematocrit value on tacrolimus pharmacokinetics in Chinese renal transplant recipients. The findings suggest that CYP3A5*3 and CYP3A4*1G polymorphisms and hematocrit are determinant factors in the apparent clearance of tacrolimus. The initial dose design is mainly based on CYP3A5 and CYP3A4 genotypes as well as hematocrit. This result may also be useful for maintenance tacrolimus dose optimization and may help to avoid fluctuating tacrolimus levels and improve the efficacy and tolerability of tacrolimus in kidney transplant recipients.

PET measurement of receptor occupancy as a tool to guide dose selection in neuropharmacology: are we asking the right questions?

Receptor occupancy studies are becoming commonplace for verifying drug mechanism of action and selecting early development candidates. Positron emission tomography (PET) has been applied to pharmacodynamic (PD) studies in several therapeutic areas including neurology, cardiology, and oncology. Prospective use of PET to define dosing requirements has been proposed particularly for central nervous system (CNS)-targeted drugs; however, correlations with clinical outcomes have been mostly anecdotal and not causally established.

Identifying novel phenotypes of vulnerability and resistance to activity-based anorexia in adolescent female rats.

OBJECTIVE: Activity-based anorexia is a translational rodent model that results in severe weight loss, hyperactivity, and voluntary self-starvation. The goal of our investigation was to identify vulnerable and resistant phenotypes of activity-based anorexia in adolescent female rats. METHOD: Sprague-Dawley rats were maintained under conditions of restricted access to food (N = 64; or unlimited access, N = 16) until experimental exit, predefined as a target weight loss of 30-35% or meeting predefined criteria for animal health. Nonlinear mixed effects statistical modeling was used to describe wheel running behavior, time to event analysis was used to assess experimental exit, and a regressive partitioning algorithm was used to classify phenotypes. RESULTS: Objective criteria were identified for distinguishing novel phenotypes of activity-based anorexia, including a vulnerable phenotype that conferred maximal hyperactivity, minimal food intake, and the shortest time to experimental exit, and a resistant phenotype that conferred minimal activity and the longest time to experimental exit. DISCUSSION: The identification of objective criteria for defining vulnerable and resistant phenotypes of activity-based anorexia in adolescent female rats provides an important framework for studying the neural mechanisms that promote vulnerability to or protection against the development of self-starvation and hyperactivity during adolescence. Ultimately, future studies using these novel phenotypes may provide important translational insights into the mechanisms that promote these maladaptive behaviors characteristic of anorexia nervosa.

Diagnosing malabsorption with systemic lipid profiling: pharmacokinetics of pentadecanoic acid and triheptadecanoic acid following oral administration in healthy subjects and subjects with cystic fibrosis.

OBJECTIVE: A Malabsorption Blood Test (MBT) is proposed as an alternative method to the 72-hour stool and dietary collection for assessing the degree of fat malabsorption in people with pancreatic insufficiency. The MBT consists of a simultaneous oral dose of pentadecanoic acid (PA), a free fatty acid, and triheptadecanoic acid (THA), a triglyceride with three heptadecanoic (HA) saturated fatty acids requiring hydrolysis by pancreatic lipase before HA can be intestinally absorbed. The aim of this study is to demonstrate the ability of MBT to detect fat malabsorption in healthy adult subjects using the pancreatic lipase (PL) inhibitor Orlistat (Xenical®), and in subjects with CF and PI while on and off routine pancreatic enzyme doses. MATERIALS AND METHODS: The MBT with the PA and THA were delivered in a breakfast test meal (2.5 g PA and either 5 g or 8 g THA) to healthy adult subjects (ages 18 - 50 years, BMI 21 - 30) and to subjects with CF (> 12 years, FEV1% predicted > 40%), after a 12-hour fast and 24 hours without dairy foods. Serum levels of PA and HA were assessed by gas-liquid chromatography, from blood samples drawn prior to MBT and then hourly for 8 hours. For healthy subjects, the MBT was administered before and after Orlistat treatment, and in subjects with CF, both with subjects receiving routine pancreatic lipase treatment ("on enzyme") and also "off enzyme" treatment. Treatment groups were compared for baseline (C0) and maximum (Cmax) plasma concentrations of PA and HA over 8 hours: area under the curve (AUC) was calculated using linear trapezoid method. The ratio of HA to PA Cmax and AUC was also calculated and compared. RESULTS: For the healthy subjects (n = 15, 60% female, ages 21 - 49 years), absorption of HA was reduced 71% for Cmax (p < 0.001) and 65% for AUC (p = 0.001) after Orlistat treatment, and absorption of PA was unchanged. For subjects with CF (n = 6, 50% female, ages 13 - 19 years), absorption of HA was minimal with subjects "off enzymes" and increased significantly with subjects "on enzymes" while absorption of PA did not differ between groups. Enzyme administration resulted in increased Cmax HA/ PA ratios from 0.02 to 0.92 and from 0.05 to 0.73 in subjects with CF receiving 5.0 g and 8.0 g of THA, respectively. AUC HA/PA ratios showed similar increases. CONCLUSIONS: In this pilot and feasibility proof-of-concept study, the MBT, utilizing the relative absorption of HA to PA, two odd-chained fatty acids, responds to changes in fat absorption in healthy subjects using a lipase inhibitor and in subjects with CF while on or off enzyme therapy. The MBT holds promise to provide a more accurate, specific and acceptable alternative to the 72-hour stool collection to quantify pancreatic-based fat malabsorption in a variety of clinical and research contexts.

Considerations for the prediction of survival time in pancreatic cancer based on registry data.

Semi-parametric and parametric survival models in patients with pancreatic adenocarcinoma (PC) using data from Surveillance, Epidemiology, and End Result (SEER) registry were developed to identify relevant covariates affecting survival, verify against external patient data and predict disease outcome. Data from 82,251 patients was extracted using site and histology codes for PC in the SEER database and refined based on specific cause of death. Predictors affecting survival were selected from SEER database; the analysis dataset included 2,437 patients. Survival models were developed using both semi-parametric and parametric approaches, evaluated using Cox-Snell and deviance residuals, and predictions were assessed using an external dataset from Saint Louis University (SLU). Prediction error curves (PECs) were used to evaluate prediction performance of these models compared to Kaplan-Meier response. Median overall survival time of patients from SEER data was 5 months. Our analysis shows that the PC data from SEER was best fitted by both semi-parametric and the parametric model with log-logistic distribution. Predictors that influence survival included disease stage, grade, histology, tumor size, radiation, chemotherapy, surgery, and lymph node status. Survival time predictions from the SLU dataset were comparable and PECs show that both semi-parametric and parametric models exhibit similar predictive performance. PC survival models constructed from registry data can provide a means to classify patients into risk-based subgroups, to predict disease outcome and aide in the design of future prospective randomized trials. These models can evolve to incorporate predictive biomarker and pharmacogenetic correlates once adequate causal data is established.

Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy.

Pregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted. Part of the challenge is the uncertain risk potential that pregnant women represent in the absence of timely and costly toxicology and developmental pharmacology studies, which only partly mitigate such risks. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed. Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Funding for translational research in pregnancy is limited but will fill some of these gaps, especially when joined with ongoing clinical trials in pregnancy that also fill certain knowledge gaps, especially biomarker and end point evaluation across pregnancy states linked to clinical outcomes. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real-world data sources and complimentary artificial intelligence/machine learning approaches. The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.